دانلود مقاله ISI انگلیسی شماره 110589
ترجمه فارسی عنوان مقاله

با استفاده از مدل های رگرسیون برای پیدا کردن توابع برای تعیین تولید برش بر اساس آزمایش های آزمایشگاهی

عنوان انگلیسی
Utilizing regression models to find functions for determining ripping production based on laboratory tests
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
110589 2017 38 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Measurement, Volume 111, December 2017, Pages 216-225

ترجمه کلمات کلیدی
تولید نابود، تست آزمایشگاهی، تجزیه و تحلیل ساده رگرسیون، رگرسیون چندگانه خطی، رگرسیون چندگانه غیر خطی،
کلمات کلیدی انگلیسی
Ripping production; Laboratory test; Simple regression analysis; Linear multiple regression; Non-linear multiple regression;
پیش نمایش مقاله
پیش نمایش مقاله  با استفاده از مدل های رگرسیون برای پیدا کردن توابع برای تعیین تولید برش بر اساس آزمایش های آزمایشگاهی

چکیده انگلیسی

The selection of suitable overburden loosening method has a crucial importance in several applications of geotechnical engineering. Factors such as rock properties and environmental constrains play a significant role in the selection of the needed equipment for overburden loosening. This paper presents several new models/equations for prediction of ripping production (Q) using rock material properties. To this end, three sites in Malaysia were selected and a total of 52 direct ripping tests were conducted in Johor state on sandstone and shale rock types. In addition, using the collected block samples, point load test, Brazilian test, slake-durability test, p-wave velocity test and uniaxial compressive strength test were also carried out in the laboratory. Numerous equations have been proposed to predict Q considering simple regression, linear multiple regression (LMR), and non-linear multiple regression (NLMR) models. Simple regression analysis indicated that the relationships between rock material properties and Q were meaningful and acceptable. Furthermore, both LMR and NLMR equations indicated similar performance capacity in predicting Q. Nevertheless; the use of NLMR equations resulted in prediction performance with higher accuracy in estimating Q compared to LMR equations.